Main results
TIER 1 - SNV/INDEL
None
TIER 2 - SNV/INDEL
None
sCNA
Not determined
TUMOR PURITY
Not provided
TUMOR PLOIDY
Not provided
MSI STATUS
MSS
DOMINANT SIGNATURE
Unknown
MUTATIONAL BURDEN
0.47 mutations/Mb
KATAEGIS EVENTS
None
The report is generated with PCGR version 1.0.2, using the following key settings:
TIER 1 - SNV/INDEL
None
TIER 2 - SNV/INDEL
None
sCNA
Not determined
TUMOR PURITY
Not provided
TUMOR PLOIDY
Not provided
MSI STATUS
MSS
DOMINANT SIGNATURE
Unknown
MUTATIONAL BURDEN
0.47 mutations/Mb
KATAEGIS EVENTS
None
For estimation of TMB, PCGR employs two different approaches/algorithms ( all_coding, and nonsyn, see details outlined in the Documentation below).
The plot below indicates how the mutational burden estimated for the query tumor sample (red dotted line) compares with the distributions observed for tumor samples in The Cancer Genome Atlas (TCGA). The grey area indicates the upper TMB tertile as defined by the user. Please note the following characteristics of the TCGA dataset presented here, which must be taken into account during TMB interpretation of the query sample:
The prioritization of SNV/InDels is here done according to a four-tiered structure, adopting the joint consensus recommendation by AMP/ACMG (Li et al. 2017).
The table below permits filtering of the total SNV/InDel set by various criteria.
NOTE 1: The filtering applies to this table only, and not to the tier-specific tables below.
NOTE 2: Filtering on sequencing depth/allelic fraction depends on input specified by user (VCF INFO tags).
NOTE - listing top 2000 variants
No variant-evidence item associations found.
No variant-evidence item associations found.
No variant-evidence item associations found.
No variant-evidence item associations found.
No variant-evidence item associations found.
No variant-evidence item associations found.
The table below lists all variants:
No variants found.
A list of ALL associations between variants in the tumor sample and known biomarkers are shown in this section, i.e. also listing biomarkers that are not assigned to TIER 1/TIER 2.
Microsatellite instability (MSI) is the result of impaired DNA mismatch repair and constitutes a cellular phenotype of clinical significance in many cancer types, most prominently colorectal cancers, stomach cancers, endometrial cancers, and ovarian cancers (Cortes-Ciriano et al., 2017). We have built a statistical MSI classifier from somatic mutation profiles that separates MSI.H (MSI-high) from MSS (MS stable) tumors. The MSI classifier was trained using 999 exome-sequenced TCGA tumor samples with known MSI status (i.e. assayed from mononucleotide markers), and obtained a positive predictive value of 98.9% and a negative predictive value of 98.8% on an independent test set of 427 samples. Details of the MSI classification approach can be found here.
The plot below illustrates the fraction of indels among all calls in CRS-FRF-2022-00160.01 (black dashed line) along with the distribution of indel fractions for TCGA samples (colorectal, endometrial, ovarian, stomach) with known MSI status assayed from mononucleotide markers ( MSI.H = high microsatellite instability, MSS = microsatellite stable)
No variants found.
The set of somatic mutations observed in a tumor reflects the varied mutational processes that have been active during its life history, providing insights into the routes taken to carcinogenesis. Exogenous mutagens, such as tobacco smoke and ultraviolet light, and endogenous processes, such as APOBEC enzymatic family functional activity or DNA mismatch repair deficiency, result in characteristic patterns of mutation. Mutational signatures can have significant clinical impact in certain tumor types (Póti et al., 2019, Ma et al., 2018)
Here, we apply the MutationalPatterns package (Blokzijl et al., 2018) to deconstruct the contribution of known mutational signatures in a single tumor sample. MutationalPatterns attempts to make an optimal reconstruction of the mutations observed in a given sample with a reference collection of n = 67 mutational signatures. By default, we restrict the signatures in the reference collection to those already observed in the tumor type in question (i.e. from large-scale de novo signature extraction on ICGC tumor samples).
Specifically, for tumors of type Cancer, NOS, mutational signature reconstruction is here limited to the following reference collection:A total of n = 1794 SNVs were used for the mutational signature analysis of this tumor.
Accuracy of signature fitting: 95.2% (reflects how well the mutational profile can be reconstructed with signatures from the reference collection)
Kataegis describes a pattern of localized hypermutations identified in some cancer genomes, in which a large number of highly-patterned basepair mutations occur in a small region of DNA (ref Wikipedia). Kataegis is prevalently seen among breast cancer patients, and it is also exists in lung cancers, cervical, head and neck, and bladder cancers, as shown in the results from tracing APOBEC mutation signatures (ref Wikipedia). PCGR implements the kataegis detection algorithm outlined in the KataegisPortal R package.
Explanation of key columns in the resulting table of potential kataegis events:
The analysis performed in the cancer genome report is based on the following underlying tools and knowledge resources:
PCGR databundle version
Software
Databases/datasets
The prioritization of SNV and InDels found in the tumor sample is done according to a four-tiered structure, adopting the joint consensus recommendation by AMP/ACMG Li et al., 2017.
A complete list of reported biomarkers that associate with variants in the tumor sample (not necessarily qualifying for assignment to TIER 1/TIER 2) is also shown in a separate section.
Somatic copy number aberrations identified in the tumor sample are classified into two main tiers:
Included in the report is also a complete list of all oncogenes subject to amplifications, tumor suppressor genes subject to homozygous deletions, and other drug targets subject to amplifications
The set of somatic mutations observed in a tumor reflects the varied mutational processes that have been active during its life history, providing insights into the routes taken to carcinogenesis. Exogenous mutagens, such as tobacco smoke and ultraviolet light, and endogenous processes, such as APOBEC enzymatic family functional activity or DNA mismatch repair deficiency, result in characteristic patterns of mutation. Mutational signatures can have significant clinical impact in certain tumor types (Póti et al., 2019, Ma et al., 2018)
The MutationalPatterns package (Blokzijl et al., 2018) is used to estimate the relative contribution of known mutational signatures in a single tumor sample. MutationalPatterns makes an optimal reconstruction of the mutations observed in a given sample with COSMIC’s (V3.2) reference collection of n = 78 mutational signatures (SBS, including sequencing artefacts). By default, we restrict the signatures in the reference collection to those already observed in the tumor type in question (i.e. from large-scale de novo signature extraction on ICGC-PCAWG tumor samples).
Specifically, for tumors of type Cancer, NOS, mutational signature reconstruction is limited to the following reference collection:The accuracy of signature fitting reflects how well the mutational profile can be reconstructed with signatures from the reference collection. Reconstructions with fitting accuracy below 90% should be interpreted with caution.
Tumor mutational load or mutational burden is a measure of the number of mutations within a tumor genome, defined as the total number of mutations per coding area of a tumour genome. TMB may serve as a proxy for determining the number of neoantigens per tumor, which in turn may have implications for response to immunotherapy. For estimation of TMB, PCGR employs two different algorithms (one to be chosen by the user):
Numbers obtained with 1) or 2) are next divided by the coding target size of the sequencing assay.
Microsatellite instability (MSI) is the result of impaired DNA mismatch repair and constitutes a cellular phenotype of clinical significance in many cancer types, most prominently colorectal cancers, stomach cancers, endometrial cancers, and ovarian cancers (Cortes-Ciriano et al., 2017). We have built a statistical MSI classifier from somatic mutation profiles that separates MSI.H (MSI-high) from MSS (MS stable) tumors. The MSI classifier was trained using 999 exome-sequenced TCGA tumor samples with known MSI status (i.e. assayed from mononucleotide markers), and obtained a positive predictive value of 100% and a negative predictive value of 99.4% on an independent test set of 427 samples. Details of the MSI classification approach can be found here.
Note that the MSI classifier is applied only for WGS/WES tumor-control sequencing assays.
Kataegis describes a pattern of localized hypermutations identified in some cancer genomes, in which a large number of highly-patterned basepair mutations occur in a small region of DNA (ref Wikipedia). Kataegis is prevalently seen among breast cancer patients, and it is also exists in lung cancers, cervical, head and neck, and bladder cancers, as shown in the results from tracing APOBEC mutation signatures (ref Wikipedia). PCGR implements the kataegis detection algorithm outlined in the KataegisPortal R package.
Explanation of key columns in the resulting table of potential kataegis events:
For PCGR reports that are fueled with CPSR report contents (JSON), we here list the main findings from the CPSR report, i.e. the collection of Pathogenic/Likely Pathogenic/VUS variants (ClinVar and novel CPSR-classified variants). We also show whether any of the query variants are associated with established biomarker evidence items with respect to cancer predisposition, prognosis, therapeutic regimens etc.
Each report is provided with a list of trials for the tumor type in question, where we limit the trials listed to ongoing or forthcoming trials with a “molecular focus” (presence of molecular biomarkers in inclusion criteria, targeted drugs as interventions etc.). Recognition of biomarkers in trials is conducted through an in-house text mining procedure.
Note that the trials have currently not been subject to any matching with respect to the molecular profile of the tumor, trials are thus basically unprioritized, and have to be explored interactively by the user in order to discover relevant trials.